인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.11 No.6
- 발행연도
- 2022.12
- 수록면
- 412 - 420 (9page)
- DOI
- 10.5573/IEIESPC.2022.11.6.412
이용수
초록· 키워드
A deep texture adaptive denoising method is proposed to achieve high perceptual image quality. Textual information is learned through a designed loss function utilizing a pre-generated texture map to distinguish textual areas from flat areas. In the training process, the proposed network internally finds texture and flat regions and differs in denoising strength in the two regions. Unlike existing DNN-based denoising methods, the proposed method retains high-frequency textual information while removing residual noise in flat regions as much as possible. The gradient distribution of the image before and after the denoising was compared. The proposed method outperformed the existing methods with higher PSNR and SSIM scores in visual quality. In addition, the strength of removing textual noise was controllable with a single parameter. Thus, the proposed method is practically feasible as a denoising apparatus.
#Image denoising
#DNN-based
#Texture-adaptive denoising
#Texture segmentation
#Perceptual image quality
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목차
- Abstract
- 1. Introduction
- 2. Related Work
- 3. The Proposed Scheme
- 4. Performance Evaluation
- 5. Conclusion
- References
참고문헌
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